Unmanned Aerial Vehicles (UAVs) are increasingly utilized in wireless communication, yet accurate channel loss prediction remains a significant challenge, limiting resource optimization performance. This paper proposes a novel AIGC (Artificial Intelligence Generated Content)-driven framework that revolutionizes UAV communication through three key innovations: data augmentation, channel prediction,...Show More
When Unmanned Aerial Vehicles (UAVs) perform high-precision communication tasks, such as searching for users and providing emergency coverage, positioning errors between base stations and users make it challenging to deploy trajectory planning algorithms. To address these challenges caused by position errors, a framework was proposed to compensate it by Channel Knowledge Map (CKM), which stores ch...Show More
In this paper, we apply the channel state in-formation(CSI) provided by an online Channel Knowledge Map(CKM), which is dynamically environment-aware, to assist the UAV in calculating the propagation loss with the ground users. We aim to jointly design the U AV trajectory, user association, and power allocation to achieve a minimum flight time under the constraints of communication demands in an em...Show More
